Duration: 1 December 2022 to 30 November 2025
Grant PCI2022-135008-2 funded by MCIN/AEI/ 10.13039/501100011033 and by European Union NextGenerationEU/PRTR
PI: Paolo Rosso
Members: Jon Ander Gómez, Carlos D. Martínez, Ana-Maria Bucur, Berta Chulvi
There is no doubt that Online Social Networks have changed the way people communicate and interact. These platforms have helped to break down barriers and to ease worldwide communication, to share multimedia content or to easily know what is happening in almost any point in the world. Facebook (2004) or Twitter (2006) but also other social media platforms such as Youtube (2005) or messaging services such as WhatsApp (2009) have promoted this new era. However, the meteoric rise of the use of these platforms has also been followed by continuous attempts to subvert their original purposes, using them by the so-called malicious actors for evil purposes. Examples of this can be the generation of mistrust against vaccines, the creation of content supporting climate denial theories, or disinformation campaigns trying to manipulate people’s opinion to alter the results of democratic elections. Therefore, there is a wide variety of different types of attempts to undermine social networks and to distorse public discourse.
In this project, we seek to reveal and detect the presence of malicious actors in Online Social Networks and to profile these actors through a multidisciplinary approach, including experts in the use of novel computational techniques from the Artificial Intelligence and Computer Vision fields and experts from the Behavioural Sciences field. The project will also leverage a multimodality approach, analysing text, images, videos, audio network structures, interactions between users and trust perceptions. From the Behavioural Sciences research field, different approaches, including psychology, discourse analysis, sociology and human-computer interaction will allow us to build a taxonomy of the different malicious actors and to profile them. From the Artificial Intelligence field, tools such as Deep Learning, advanced Natural Language Processing, and Social Network Analysis provide us with the necessary instruments to build a software tool for the analysis and detection of these malicious actors with novel features such as multilingualism, explainability, and a strong focus on an open-source solution.